Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate automatic and semi-automatic delineation of regions of interest within images, aiding in accurate segmentation. This paper provides a comprehensive analysis of recent works leveraging spatial logic to develop operators and tools for identifying regions of interest, including tumorous and non-tumorous areas. Additionally, we examine the challenges inherent to spatial model-checking techniques, such as variability in ground truth data and the need for streamlined procedures suitable for routine clinical practice.
@article{arxiv.2501.02024,
title = {Model Checking in Medical Imaging for Tumor Detection and Segmentation},
author = {Elhoucine Elfatimi and Lahcen El fatimi},
journal= {arXiv preprint arXiv:2501.02024},
year = {2025}
}